LSTM和CNN在引力波实数据核坍缩超新星搜索中的应用
LSTM and CNN application for core-collapse supernova search in gravitational wave real data
摘要：
$Context.$核心坍缩超新星（CCSNe）预计会发出引力波信号，这些信号可以被银河系和附近星系内的当前和未来一代干涉仪探测到。从CCSNe产生的信号的随机性质需要替代检测方法来匹配滤波。$Aims.$我们的目标是显示机器学习（ML）的潜力，不同的CCSNe模拟信号和噪声瞬态使用真实数据的多标签分类。我们比较了一维和二维卷积神经网络（CNN）在单检测器和多检测器数据上的性能。这是我们第一次使用长短期记忆（LSTM）网络测试多标签分类。$Methods.$我们使用事件触发发生器，小波检测滤波器（WDF），结合ML，对CCSNe信号进行搜索和分类。我们使用数据的时间序列和时频表示作为ML模型的输入。为了计算分类精度，我们同时注入，在可检测的距离为1\，kpc，CCSN波形，从最近的中微子驱动的核心崩溃的流体动力学模拟，干涉仪噪声从O2 LIGO和处女座科学运行。$Results.$我们比较了三种模型在单检测器数据上的性能。然后，我们合并了噪声和天体物理瞬变的单探测器分类模型的输出，获得了LIGO（$\sim99\%$）和室女座（$\sim80\%$）的总体精度。我们将我们的分析扩展到多探测器的情况下，使用三个ITF之间的触发器重合，并实现了$\sim98\%$的准确性。
英文摘要：
$Context.$ Core-collapse supernovae (CCSNe) are expected to emit gravitational wave signals that could be detected by current and future generation interferometers within the Milky Way and nearby galaxies. The stochastic nature of the signal arising from CCSNe requires alternative detection methods to matched filtering. $Aims.$ We aim to show the potential of machine learning (ML) for multi-label classification of different CCSNe simulated signals and noise transients using real data. We compared the performance of 1D and 2D convolutional neural networks (CNNs) on single and multiple detector data. For the first time, we tested multi-label classification also with long short-term memory (LSTM) networks. $Methods.$ We applied a search and classification procedure for CCSNe signals, using an event trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used time series and time-frequency representations of the data as inputs to the ML models. To compute classification accuracies, we simultaneously injected, at detectable distance of 1\,kpc, CCSN waveforms, obtained from recent hydrodynamical simulations of neutrino-driven core-collapse, onto interferometer noise from the O2 LIGO and Virgo science run. $Results.$ We compared the performance of the three models on single detector data. We then merged the output of the models for single detector classification of noise and astrophysical transients, obtaining overall accuracies for LIGO ($\sim99\%$) and ($\sim80\%$) for Virgo. We extended our analysis to the multi-detector case using triggers coincident among the three ITFs and achieved an accuracy of $\sim98\%$.
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